Deceased Humpback Whale - Joshua Meza-Fidalgo, 2020

Deceased Humpback Whale - Joshua Meza-Fidalgo, 2020

Rationale and Research Questions

Marine mammal and sea turtle strandings can be unusual events or indicators of problems in our ocean ecosystems. Seeing patterns in the strandings of marine mammals and sea turtles can be indicative of more complex issues that may not be transparent. Some example of these issues could be viral outbreaks in lower trophic level populations or toxic algal blooms that kill off all sea life in the area. Because humans demand and consume seafood, understanding the problems that arise are essential to preventing the consumption of contaminated seafood along with the management of fisheries. By studying marine mammal and sea turtle strandings, we get insight on the health of our oceans.

Our objective is to analyze animal strandings over the years to see if there any trends in the quantity of strandings for each family (cetaceans, odontocetes, mysticetes, and pinnipeds). We have decided to look at the total number of strandings per year for all families, the changes in strandings over the years for each family and the seasonality differences across all years for each family.

Dataset Information

Table 1: Dataset Information
Detail Description
Data Source OBIS-SEAMAP - Mystic Aquarium
Retrieved From https://seamap.env.duke.edu/dataset/945
Variables Used Family, Common Name, Species Name, Date, Year, Month, Latitude, Longitude, Total Strandings, Strandings by Species
Data Range March 18, 1976 to December, 29, 2011

The dataset that we chose to work with is the Mystic Aquarium’s marine mammal and sea turtle stranding data that started in 1976 until 2011. However, there was only one stranding record in 1976, so that point was excluded from our analysis. The rest of the data started in 1990 and was concluded in 2011. It contains the number of whale (odontocetes and mysticetes), pinniped, and sea turtle strandings that occurred along the coasts of Connecticut, Rhode Island and Fishers Island, New York, USA. There were a total of 1140 strandings; within these strandings, 155 were odontocetes, 50 were mysticetes, 679 were pinnipeds, and 256 were sea turtles.

The dataset has a latitude that ranges from 41.00 to 41.86 while the longitude ranges from -73.65 to -71.10. We data was provided using the datum WGS 1984 (4326) and we performed a data transformation into UTM 19 (32619) for geospatial analysis. The data was wrangled to contain only the variables that we were interested in using. We then selected parts of the data based off of the Common Name of the species to create datasets that were easier for us to use in our analysis. This helped us divide the data into four groups: odontocetes, mysticetes, pinnipeds, and sea turtles. We initially looked at total yearly strandings for trends in the data for each family. After we analyzed the data on a monthly basis to help give insight to seasonal stranding trends for each family.

Figure 1: The Column Names of the Raw Data 
##  [1] "dataset_id"      "row_id"          "latitude"        "longitude"      
##  [5] "species_name"    "scientific_name" "common_name"     "itis_tsn"       
##  [9] "group_size"      "series_id"       "date_time"       "timezone"       
## [13] "ds_type"         "platform"        "provider"        "lprecision"     
## [17] "tprecision"      "oceano"          "notes"           "last_mod"
Figure 2: The Column Names of the Processed Data 
## [1] "Family"       "common_name"  "species_name" "Date"         "Year"        
## [6] "Month"        "latitude"     "longitude"
Table 2: The First Rows and Columns of the Final Processed Data
Table 3: The First Rows and Columns of Strandings of Each Family per Year
Table 4: The First Rows and Columns of Strandings of Each Family per Month

Exploratory Analysis

We initially decided that it would be best to look at the data holistically to get a better feel for the trends in the data. We first analyzed the total number of strandings per each family, as seen in Figure 3. To better see the data, we divided summed the strandings of each family for each year from 1990 to 2011, as seen in Figure 4. To see if there could be seasonal trends for the entire data set, the data was grouped by month for each family from the years 1990 to 2011, as seen in Figure 5. Finally, to have a geospatial understanding of our data, Map 1 was created.

Figure 3: Total Strandings per Family

Figure 4: Total Strandings per Year

Figure 5: Total Strandings per Month

Map 1: Geospatial Exploration of each Family

Analysis

NOTE– Did we ever do an analysis for the entire data set without dividing it by family????

Because we decided to divide the data into groups by their families, we conducted separate analysis for each of the families. Our research questions are the following:

  1. Pinnipeds:

#Pinniped Data Exploration

##          Family                          common_name 
##  Mysticetes :  0   Harp Seal                   :272  
##  Odontocetes:  0   Harbor Seal                 :209  
##  Pinnipeds  :679   Atlantic gray seal          :107  
##  Turtles    :  0   pinnipeds                   : 58  
##                    Hooded Seal                 : 33  
##                    Atlantic White-sided Dolphin:  0  
##                    (Other)                     :  0  
##                        species_name      Date                 Year     
##  Harp Seal                   :272   Min.   :1990-03-07   Min.   :1990  
##  Harbor Seal                 :209   1st Qu.:1997-05-05   1st Qu.:1997  
##  Grey Seal                   :107   Median :2002-02-14   Median :2002  
##  Unknown Species             : 58   Mean   :2002-02-22   Mean   :2002  
##  Hooded Seal                 : 33   3rd Qu.:2006-08-03   3rd Qu.:2006  
##  Atlantic White Sided Dolphin:  0   Max.   :2011-12-29   Max.   :2011  
##  (Other)                     :  0                                      
##      Month         latitude       longitude     
##  Min.   : 1.0   Min.   :41.02   Min.   :-73.57  
##  1st Qu.: 3.0   1st Qu.:41.32   1st Qu.:-71.85  
##  Median : 4.0   Median :41.37   Median :-71.55  
##  Mean   : 4.3   Mean   :41.39   Mean   :-71.68  
##  3rd Qu.: 5.0   3rd Qu.:41.48   3rd Qu.:-71.37  
##  Max.   :12.0   Max.   :41.86   Max.   :-71.12  
##

Pinniped data exploration indicates a possible overall increasing trend in pinniped strandings and a possible monthly/seasonal trend. There is some fluctuation in number of total yearly strandings both for the family as a whole and at the species level, although a more detailed species level analysis is beyond the scope of this project. Harp and harbor seals make up most of the strandings each year. Most yearly strandings occur in the late winter and spring time, particularly in March, with very few in the late summer through the early winter. Springtime coincides with the pupping seasons for many seal species and could be a factor in this.

Pinniped strandings are most numerous throughout the coastline of Rhode Island and the eastern coastline of Connecticut.

#Pinniped Stranding Analysis

#Pinniped Statisical Analysis

Null Hypothesis 1: There is no effect of year on the total number of pinniped strandings Alternative Hypothesis 1: There is an effect of year on the total number of pinniped strandings

Null Hypothesis 2: There is no effect of month on the total number of pinniped strandings Alternative Hypothesis 2: There is an effect of month on the total number of pinniped strandings

## 
## Call:
## glm(formula = Pinnipeds_Total ~ factor(Year), family = poisson, 
##     data = Annual_Pinniped_Strandings)
## 
## Deviance Residuals: 
##  [1]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## Coefficients:
##                  Estimate Std. Error z value Pr(>|z|)    
## (Intercept)        2.1972     0.3333   6.592 4.35e-11 ***
## factor(Year)1991   0.3677     0.4336   0.848 0.396429    
## factor(Year)1992   0.2877     0.4410   0.652 0.514142    
## factor(Year)1993   0.2877     0.4410   0.652 0.514142    
## factor(Year)1994   1.1701     0.3816   3.066 0.002166 ** 
## factor(Year)1995   1.2368     0.3786   3.266 0.001090 ** 
## factor(Year)1996   1.5640     0.3666   4.267 1.98e-05 ***
## factor(Year)1997   1.0986     0.3849   2.854 0.004313 ** 
## factor(Year)1998   1.6314     0.3645   4.476 7.61e-06 ***
## factor(Year)1999   0.8473     0.3984   2.127 0.033445 *  
## factor(Year)2000   0.9808     0.3909   2.509 0.012095 *  
## factor(Year)2001   2.0369     0.3544   5.747 9.07e-09 ***
## factor(Year)2002   1.3291     0.3749   3.546 0.000392 ***
## factor(Year)2003   1.1350     0.3832   2.962 0.003056 ** 
## factor(Year)2004   1.6529     0.3639   4.543 5.55e-06 ***
## factor(Year)2005   1.3291     0.3749   3.546 0.000392 ***
## factor(Year)2006   1.3581     0.3737   3.634 0.000279 ***
## factor(Year)2007   0.9383     0.3932   2.386 0.017016 *  
## factor(Year)2008   1.0217     0.3887   2.628 0.008584 ** 
## factor(Year)2009   1.4663     0.3698   3.965 7.33e-05 ***
## factor(Year)2010   1.2993     0.3761   3.455 0.000550 ***
## factor(Year)2011   1.6094     0.3651   4.408 1.05e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 1.3603e+02  on 21  degrees of freedom
## Residual deviance: 1.3101e-14  on  0  degrees of freedom
## AIC: 157.57
## 
## Number of Fisher Scoring iterations: 3
## 
## Call:
## glm(formula = Pinnipeds_Total ~ factor(Month), family = poisson, 
##     data = Monthly_Pinniped_Strandings)
## 
## Deviance Residuals: 
##  [1]  0  0  0  0  0  0  0  0  0  0  0  0
## 
## Coefficients:
##                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)       3.7377     0.1543  24.223  < 2e-16 ***
## factor(Month)2    0.5528     0.1937   2.854 0.004313 ** 
## factor(Month)3    1.3743     0.1727   7.957 1.77e-15 ***
## factor(Month)4    1.3058     0.1740   7.506 6.09e-14 ***
## factor(Month)5    0.8267     0.1850   4.468 7.88e-06 ***
## factor(Month)6    0.4670     0.1968   2.373 0.017647 *  
## factor(Month)7   -0.4055     0.2440  -1.662 0.096530 .  
## factor(Month)8   -1.4351     0.3519  -4.078 4.53e-05 ***
## factor(Month)9   -1.7918     0.4082  -4.389 1.14e-05 ***
## factor(Month)10  -1.7918     0.4082  -4.389 1.14e-05 ***
## factor(Month)11  -1.1727     0.3174  -3.695 0.000220 ***
## factor(Month)12  -1.0296     0.3008  -3.423 0.000619 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance:  5.9537e+02  on 11  degrees of freedom
## Residual deviance: -3.5527e-15  on  0  degrees of freedom
## AIC: 87.913
## 
## Number of Fisher Scoring iterations: 3

We reject the null hypothesis that there is no effect of year on the total number of pinniped strandings. 18 of the 21 documented years were statistically significant with p-values ranging from 9.07e-09(2001) to 0.03(1999). Only the first three years of data, 1991-1993 had p-values above 0.05. The null deviance of yearly model was 1.36e+02 on 21 degrees of freedom, and residual deviance of 1.31e-14

We reject the null hypothesis that there is no effect of month on the total number of pinniped strandings. 11 of the 12 months had p-values of less than 0.05, with July being the exception. The p-values of the statistically significant month ranged from 1.77e-15(March) to 0.02(June). The null deviance for the monthly model was 5.95e+02 on 11 degrees of freedom and a residual deviance of -3.55e-15.

This information can lead to further study as to why years and months differ and develop ways to predict and/or mitigate particularly bad years and/or months for pinnped strandings.

#Pinniped Geospatial Analysis

#Converting the data points to UTM for further analysis. The study are straddles UTM zones 18 and 19, zone 19 was chosen because that is where the greatest concentration of points seems to be located.

Pinniped_sf_utm <- st_transform(Pinniped_sf,32619)

Pinniped_sf_utm$geometry
## Geometry set for 679 features 
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 115345.6 ymin: 4551593 xmax: 322648.9 ymax: 4637243
## Projected CRS: WGS 84 / UTM zone 19N
## First 5 geometries:
## POINT (293586.9 4585397)
## POINT (297917.1 4595261)
## POINT (291463.9 4582510)
## POINT (297812.2 4601441)
## POINT (310031.7 4594484)
#Determining "mean" stranding point for distance comparisons
long_mean <- mean(Pinniped_Stranding_Data$longitude)
lat_mean <- mean(Pinniped_Stranding_Data$latitude)

Pinniped_Stranding_Mean_Point <- data.frame(long_mean, lat_mean)

Pinniped_Mean_sf <- Pinniped_Stranding_Mean_Point %>%
  st_as_sf(coords = c("long_mean", "lat_mean"), crs = 4326)

Pinniped_Mean_UTM <- st_transform(Pinniped_Mean_sf,32619)

#Creating a distance matrix for further analysis
Pinniped_Distance_Matrix <- st_distance(Pinniped_sf_utm, Pinniped_sf_utm, pairwise = T)

mean_distance <-  rowMeans(Pinniped_Distance_Matrix)

Pinniped_sf_utm  <-  Pinniped_sf_utm %>% 
  bind_cols(mean_distance = mean_distance)
#Visualizing stranding point distance from "mean"
mapview(Pinniped_sf_utm, 
        zcol = "mean_distance",
        layer.name = "Distance (m) from Mean Stranding Point")
#Developing a mean distance table

# points within 40,000m
Pinniped_40 <- Pinniped_sf_utm %>%
  filter(mean_distance < 40000) %>%
  mutate(distance_group = "< 40,000")

# points between 40,000m - 60,000m
Pinniped_40_60 <- Pinniped_sf_utm %>%
  filter(mean_distance >= 40000 & mean_distance < 60000) %>%
  mutate(distance_group = "40,000 - 60,000")

# points between 60,000m - 80,000m
Pinniped_60_80 <- Pinniped_sf_utm %>%
  filter(mean_distance >= 60000 & mean_distance < 80000) %>%
  mutate(distance_group = "60,000 - 80,000")

# points between 80,000m - 100,000m
Pinniped_80_100 <- Pinniped_sf_utm %>%
  filter(mean_distance >= 80000 & mean_distance < 100000) %>%
  mutate(distance_group = "80,000 - 100,000")

# points between 100,000m - 120,000m
Pinniped_100_120 <- Pinniped_sf_utm %>%
  filter(mean_distance >= 100000 & mean_distance < 120000) %>%
  mutate(distance_group = "100,000 - 120,000")

# points between 120,000m - 140,000m
Pinniped_120_140 <- Pinniped_sf_utm %>%
  filter(mean_distance >= 120000 & mean_distance < 140000) %>%
  mutate(distance_group = "120,000 - 140,000")

# points between 140,000m - 160,000m
Pinniped_140_160 <- Pinniped_sf_utm %>%
  filter(mean_distance >= 140000 & mean_distance < 160000) %>%
  mutate(distance_group = "140,000 - 160,000")

# points greater than 160,000m
Pinniped_160 <- Pinniped_sf_utm %>%
  filter(mean_distance >= 160000) %>%
  mutate(distance_group = "> 160,000")

# row bind the distance dataframes
Pinniped_sf_utm_final <- rbind(Pinniped_40, Pinniped_40_60, Pinniped_60_80, Pinniped_80_100, Pinniped_100_120, Pinniped_120_140, Pinniped_140_160, Pinniped_160)

Pinniped_distances <- Pinniped_sf_utm_final %>%
  group_by(distance_group) %>%
  summarise(totals_by_distance = n()) %>%
  st_drop_geometry() %>%
  arrange(totals_by_distance)

kable(Pinniped_distances)
distance_group totals_by_distance
> 160,000 3
100,000 - 120,000 7
140,000 - 160,000 7
120,000 - 140,000 11
80,000 - 100,000 11
60,000 - 80,000 15
40,000 - 60,000 200
< 40,000 425

By finding the “mean” stranding location based on the all of the data points it can be determined how far each stranding point is from the mean. By determining that 425 out of 679, or 62.5 percent of, pinniped strandings occur within 40km of the mean stranding location, what makes this area a hotspot can be studied and hopefully also determined. This data also could allow for reallocation of stranding resources and personnel to this range in order to put them to the most efficient use possible. Since only 54, or 8 percent of, pinniped strandings occur further than 60km from the stranding mean, resources for study and rescue can be allocated away from those areas.

#Pinniped Temporal Analysis

Null Hypothesis: The yearly and/or monthly/seasonal pinniped stranding data is stationary Alternative Hypothesis: The yearly and/or monthly/seasonal pinniped stranding data is not stationary

#Developing a monthly time series to determine if there is a monthly/seasonal trend.
f_month <- month(first(Pinniped_Stranding_Totals$Date))
f_year <- year(first(Pinniped_Stranding_Totals$Date))


Pinniped_ts_monthly <- ts(Pinniped_Stranding_Totals$Total, 
                  start = c(f_year, f_month), 
                  frequency = 12)
 
Pinniped_ts_monthly_decomp <- stl(Pinniped_ts_monthly, s.window = "periodic")
plot(Pinniped_ts_monthly_decomp)

Pinniped_ts_monthly_trend <- Kendall::SeasonalMannKendall(Pinniped_ts_monthly)

summary(Pinniped_ts_monthly_trend)
## Score =  -163 , Var(Score) = 54217
## denominator =  6740.981
## tau = -0.0242, 2-sided pvalue =0.48391

We fail to reject the H0 that monthly pinniped strandings are stationary, with a p-value of 0.48, but there could be some seasonality based on the decomposition

#Developing a yearly time series to determine if there is a trend
Pinniped_ts_yearly <- ts(Pinniped_Stranding_Totals, start = c(1990,1), frequency = 1)

Pinniped_ts_yearly_trend <- Kendall::SeasonalMannKendall(Pinniped_ts_yearly)

summary(Pinniped_ts_yearly_trend)
## Score =  -173389 , Var(Score) = 163714576
## denominator =  615460.9
## tau = -0.282, 2-sided pvalue =< 2.22e-16

We reject the H0 that yearly pinniped strandings are stationary with a p-value or 2.22e-16. This analysis fits with the trend line plotting in the exploration of the pinniped data.

  1. Whales: -Odontocetes

#Odontocete Data Exploration

##          Family                          common_name
##  Mysticetes :  0   Short-beaked Common Dolphin :52  
##  Odontocetes:155   Harbor Porpoise             :38  
##  Pinnipeds  :  0   Atlantic White-sided Dolphin:14  
##  Turtles    :  0   Long-finned Pilot Whale     :13  
##                    toothed whales              :12  
##                    Striped Dolphin             : 9  
##                    (Other)                     :17  
##                        species_name      Date                 Year     
##  Common dolphin              :52    Min.   :1990-02-10   Min.   :1990  
##  Harbor porpoise             :38    1st Qu.:1999-05-20   1st Qu.:1999  
##  Atlantic White Sided Dolphin:14    Median :2004-09-05   Median :2004  
##  Long-finned pilot whale     :13    Mean   :2003-08-24   Mean   :2003  
##  Unidentified Odontocete     :12    3rd Qu.:2009-02-16   3rd Qu.:2009  
##  Striped dolphin             : 9    Max.   :2011-12-16   Max.   :2011  
##  (Other)                     :17                                       
##      Month           latitude       longitude     
##  Min.   : 1.000   Min.   :41.00   Min.   :-73.65  
##  1st Qu.: 3.000   1st Qu.:41.30   1st Qu.:-71.58  
##  Median : 5.000   Median :41.42   Median :-71.45  
##  Mean   : 6.065   Mean   :41.40   Mean   :-71.55  
##  3rd Qu.: 8.000   3rd Qu.:41.49   3rd Qu.:-71.30  
##  Max.   :12.000   Max.   :41.77   Max.   :-71.13  
##

There appears to be an increasing trend in the number of odontocete strandings as the years progress. There may be possible seasonal trend based on season, where the spring and early summer see a greater number of strandings than the other seasons.

#Odontocete Analysis ##Statisical Analysis

Null Hypothesis 1: There is no effect of year on the total number of odontocete strandings Alternative Hypothesis 1: There is an effect of year on the total number of odontocete strandings

Null Hypothesis 2: There is no effect of month on the total number of odontocete strandings Alternative Hypothesis 2: There is an effect of month on the total number of odontocete strandings

## Call:
##    aov(formula = Odontocetes_Total ~ Year, data = annual_odontocetes_strandings)
## 
## Terms:
##                      Year Residuals
## Sum of Squares  195.90316  91.05138
## Deg. of Freedom         1        20
## 
## Residual standard error: 2.133675
## Estimated effects may be unbalanced
##             Df Sum Sq Mean Sq F value   Pr(>F)    
## Year         1 195.90  195.90   43.03 2.16e-06 ***
## Residuals   20  91.05    4.55                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Call:
##    aov(formula = Odontocetes_Total ~ Month, data = monthly_odontocetes_strandings)
## 
## Terms:
##                    Month Residuals
## Sum of Squares   31.8619  459.0548
## Deg. of Freedom        1        10
## 
## Residual standard error: 6.775358
## Estimated effects may be unbalanced
##             Df Sum Sq Mean Sq F value Pr(>F)
## Month        1   31.9   31.86   0.694  0.424
## Residuals   10  459.1   45.91

We reject the null hypothesis that there is no effect of year on the total number of odontocete strandings due to a p-value of 2.16e-06. We also fail to reject the null hypothesis that there is no effect of month on the total number of odontocete strandings due to a p-value of 0.424. This indicates that there is a difference in the number of odontocete strandings across the years of the study, but not across the months. This could be explained by increase boat and shipping traffic as the years progress that contribute to increased ocean noise and interference with their communication that have led them to become stranded. Seasonality may not have an effect because of the consistency of boat and ship traffic throughout each year.

#Geospatial Analysis

Knowing where the “mean” stranding location is could allow for a study of that area in order to determine why odontocete strandings are so prominent on this area and allow for the allocation of more recovery resources and personnel to that area to compensate for the higher levels of strandings. Much of the strandings occurred from Charlestown to Newport, in the Rhode Island Sound.

distance_group totals_by_distance
140,000 - 160,000 1
60,000 - 80,000 1
120,000 - 140,000 2
> 160,000 3
100,000 - 120,000 3
40,000 - 60,000 20
< 40,000 125

#Temporal Analysis Null Hypothesis: The odontocetes stranding data is stationary Alternative Hypothesis: The odontocetes stranding data is not stationary

## WARNING: Error exit, tauk2. IFAULT =  12
## WARNING: Error exit, tauk2. IFAULT =  12
## WARNING: Error exit, tauk2. IFAULT =  12
## WARNING: Error exit, tauk2. IFAULT =  12
## WARNING: Error exit, tauk2. IFAULT =  12
## WARNING: Error exit, tauk2. IFAULT =  12
## WARNING: Error exit, tauk2. IFAULT =  12
## WARNING: Error exit, tauk2. IFAULT =  12
## WARNING: Error exit, tauk2. IFAULT =  12
## Score =  12 , Var(Score) = 235.3333
## denominator =  102.2623
## tau = 0.117, 2-sided pvalue =0.43407

Because the 2-sided p-value was 0.434, we fail to reject the null hypothesis and say that odontocetes monthly stranding data is stationary.

## Score =  -11194 , Var(Score) = 2672700
## denominator =  39279
## tau = -0.285, 2-sided pvalue =7.5333e-12

Because the 2-sided p-value was less 7.53e-12, we reject the null hypothesis and say that odontocete yearly stranding data is not stationary.

3. Sea Turtles:

Turtle Data Exploration

##          Family                    common_name 
##  Mysticetes :  0   Leatherback Sea Turtle:176  
##  Odontocetes:  0   Loggerhead Sea Turtle : 63  
##  Pinnipeds  :  0   Kemp's Ridley         :  9  
##  Turtles    :256   Green Sea Turtle      :  4  
##                    Turtles               :  4  
##                    Atlantic gray seal    :  0  
##                    (Other)               :  0  
##                               species_name      Date                 Year     
##  Leatherback - Dermochelys coriacea :176   Min.   :1990-09-15   Min.   :1990  
##  Loggerhead - Caretta caretta       : 63   1st Qu.:1995-08-15   1st Qu.:1995  
##  Kemp's ridley - Lepidochelys kempii:  5   Median :2000-09-13   Median :2000  
##  Kemp's Ridley - Lepidochelys kempii:  4   Mean   :2001-05-31   Mean   :2001  
##  Unknown/Unidentified               :  4   3rd Qu.:2008-02-12   3rd Qu.:2007  
##  Green turtle - Chelonia mydas      :  3   Max.   :2011-09-22   Max.   :2011  
##  (Other)                            :  1                                      
##      Month          latitude       longitude     
##  Min.   : 2.00   Min.   :41.00   Min.   :-73.57  
##  1st Qu.: 8.00   1st Qu.:41.27   1st Qu.:-71.78  
##  Median : 8.00   Median :41.35   Median :-71.55  
##  Mean   : 8.34   Mean   :41.36   Mean   :-71.66  
##  3rd Qu.: 9.00   3rd Qu.:41.47   3rd Qu.:-71.36  
##  Max.   :12.00   Max.   :41.75   Max.   :-71.10  
##

Exploratory analysis of the sea turtle stranding data set by year shows higher amounts of strandings in the early 1990s and late 2000s, with the highest amount in 1995. The most prominent species throughout our study period appear to be leatherback sea turtles, followed by loggerhead sea turtles. Through monthly data exploration, strandings peak from July to September and occur less frequently the remainder of the year.

Turtle Spatial Analysis

Turtle Spatial Table

Distance (m) Number of Strandings
less than 20,000 92
20,000 - 40,000 113
40,000 - 60,000 32
60,000 - 80,000 2
80,000 - 100,000 4
100,000 - 120,000 2
120,000 - 140,000 6
140,000 - 160,000 4
greater than 160,000 1

Of the 256 sea turtle strandings used in our analysis, 113 of them occured within 20,000 - 40,000m of the mean stranding location, followed by 92 strandings within 20,000m of the mean location (Table 1). These points were in the areas around Charlestown, RI, Newport, RI and New Shoreham, RI. This high concentration of strandings can indicate that these areas may be sea turtle stranding hotspots. This could be due to various factors, such as the flow of oceanic currents, anthropogenic impacts (such as higher fishing presrure), or the fact that there may be more manpower employed in these areas that could make stranding detection easier. Further research is needed to determine how these underlying factors may be contibuting to sea turtle strandings in these areas.

Turtle Statistical Analysis

Null Hypothesis 1: There is no effect of individual year on sea turtle strandings.
Alternative Hypothesis 1: There is an effect of individual year on sea turtle strandings.

Null Hypothesis 2: There is no effect of month on sea turtle strandings.
Alternative Hypothesis 2: There is an effect of month on sea turtle strandings.

Yearly Analysis: We reject the null hypothesis that individual year has no effect on sea turtle strandings. The sea turtle strandings in 9 of the 21 years are not significantly different (p-values: 0.147 - 0.706). 1995 was the most significantly different (p-value: 0.000236). 1991 (p-value: 0.0056), 1993 (p-value: 0.0078), 2008 (p-value: 0.0022), 2010 (p-value: 0.0016), and 2011 (p-value: 0.003) were the next most statitistically different years. The null deviance of this model is 7.4606e+01, which indicates ??? The residual deviance of this model is -4.8850e-15, which indicates ???

Monthly Analysis: We reject the null hypothesis that month has no effect on sea turtle strandings. Of the 12 months, 5 of months were significantly different. May (p-value: 0.000269), June (p-value: 3.98e-06), July (p-value: 1.91e-05), August (p-value: 0.004106), and September (p-value: 0.049935) were significantly different. This indicates that sea turtle strandings are concentrated to the summer months in our study area. The null deviance of this model is 4.9235e+02, which indicates ??? The residual deviance of this model is 3.0332e-10, which indicates ???

Turtle Time Series

Null hypothesis:
Alternative hypothesis:

Summary and Conclusions

Deceased Fin Whale - Joshua Meza-Fidalgo, 2020

Deceased Fin Whale - Joshua Meza-Fidalgo, 2020

References

Data: https://seamap.env.duke.edu/dataset/945

  1. Halpin, P.N., A.J. Read, E. Fujioka, B.D. Best, B. Donnelly, L.J. Hazen, C. Kot, K. Urian, E. LaBrecque, A. Dimatteo, J. Cleary, C. Good, L.B. Crowder, and K.D. Hyrenbach. 2009. OBIS-SEAMAP: The world data center for marine mammal, sea bird, and sea turtle distributions. Oceanography. 22(2):104-115.

  2. Smith, A. 2014. Mystic Aquarium’s marine mammal and sea turtle stranding data 1976-2011. Data downloaded from OBIS-SEAMAP (http://seamap.env.duke.edu/dataset/945) on 2022-04-02.

Photos: Special thanks to Joshua Meza-Fidalgo for providing whale photos for us to use.